Beyond Data Science

Back in 2006, Clive Humby, UK Mathemetician and architect of Tesco’s Clubcard, looked into the mountains of information that Tesco had at its fingertips and coined a new phrase: “Data is the new oil. It’s valuable, but if unrefined it cannot really be used.”

It has taken us over a decade to build the expertise, technology and insight to begin to capitalize on Humby’s observation. After all, it’s not an easy undertaking to create a new industry.

Earlier this year, the incredible growth in job postings for data scientists was revealed, with year-on-year growth hovering around 18%. Going back a couple of years, we can see that from January 2015 to January 2018, job postings for data scientists rose by 75%.

But it’s not just about jobs and roles. And it’s not just about technology. Sure we need these working together, but leaders need to think strategically and act with a long term vision in mind. And that means we also need to think about culture.

As Foteini Agrafioti, Chief Science Officer at RBC and Head of Borealis AI, explains in a recent HBR article, if we are going to be serious about the future, we need to look beyond data science towards AI:

To go beyond data science and do real AI, you need to hire the right people, embrace research, and adapt your culture.

And adapting your culture is where the opportunity with data and research becomes both interesting and challenging.

As Agrafioti explains, the current state of AI and data science means that the most sought after talent and expertise currently resides in the academic world. For leaders to attract this talent to your business or industry, there will be challenges.

Does you organization or industry engage in “wicked problems”? These difficult or impossible to solve problems are the kinds that attract the brightest minds and best talent.

[Academics] … share values that are built on both the ethos of solving big, meaningful problems and having the ability to publish the results of their efforts. Researchers take pride in contributions they make, so these factors must be in place. This translates into reproducing some of the working conditions they’ve brought from academia while allowing the transparency of collaboration and open publication that serves to advance their community as a whole.

There are certainly substantial challenges facing business leaders today – but the open, collegiate approach to knowledge sharing of the academic world often conflicts with the business need for patents and competitive advantage.

But if we want to extract the value of data, we are going to need that talent. And that means we’ll need to find new accommodations. The race is on.

Nina Nets It Out: It’s clear there is value in data. But extracting – or “mining” that data for value is no easy task. An often overlooked key to data science and our ability to move beyond, is the role of expertise and talent. Leaders seeking new value must look beyond data and create the cultural conditions that attracts the brightest minds in a competitive market.